{"id":"W2772060786","doi":"10.1109/cw.2017.35","title":"Adaptive Face Recognition Based on Image Quality","year":2017,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer science; Facial recognition system; Normalization (sociology); Pattern recognition (psychology); Discrete cosine transform; Computer vision; Discrete wavelet transform; Histogram; Adaptive histogram equalization; Face (sociological concept); Histogram equalization; Wavelet transform; Wavelet; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000677485,0.0003160031,0.0004524793,0.000769686,0.0001362594,0.0004682484,0.0004255546,0.0002658962,0.001081419],"category_scores_gemma":[0.002483077,0.0001447087,0.0003490186,0.0004815891,0.0003097399,0.00072741,0.0003989314,0.0003521459,0.0004489546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003036029,"about_ca_system_score_gemma":0.0001993558,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001160924,"about_ca_topic_score_gemma":0.001086869,"domain_scores_codex":[0.9993556,0.00007829681,0.0000270641,0.0001436491,0.0003484665,0.00004695481],"domain_scores_gemma":[0.9990776,0.0002426815,0.0001042845,0.00009494764,0.0004512726,0.00002918986],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003630321,0.00008941927,0.00657771,0.0001621036,0.00007846842,0.0001335605,0.00009272037,0.03159888,0.3029888,0.001123121,0.001163882,0.6556283],"study_design_scores_gemma":[0.00003389452,0.0003395005,0.04636224,0.00003492845,0.0001277798,0.0009330706,0.0000797087,0.7940296,0.1540916,0.001499357,0.002385605,0.00008278598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2344382,0.001230285,0.7584847,0.0001588432,0.000150284,0.0001418417,0.0001260851,0.00107258,0.004197327],"genre_scores_gemma":[0.8671441,0.0009181021,0.1289688,0.00007269794,0.00007214143,0.00006991687,0.0001892225,0.0000691536,0.002495894],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001160924,"threshold_uncertainty_score":0.003617644,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09066360916732008,"score_gpt":0.3315434691153908,"score_spread":0.2408798599480707,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}